System for uniquely identifying subjects from a target population

ABSTRACT

The system for uniquely identifying subjects from a target population operates to acquire, process and analyze images to create data which contains indicia sufficient to uniquely identify an individual in a population of interest. This system implements an automated, image-based process that captures data indicative of a selected set of external characteristics for subjects that are members of a target population of a predetermined species.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/480,526, filed Sep. 8, 2014, entitled “SYSTEM FOR UNIQUELYIDENTIFYING SUBJECTS FROM A TARGET POPULATION,” now U.S. Pat. No.9,295,227, which is a continuation of U.S. patent application Ser. No.13/850,139, filed Mar. 25, 2013, entitled “SYSTEM FOR UNIQUELYIDENTIFYING SUBJECTS FROM A TARGET POPULATION,” now U.S. Pat. No.8,826,863, which is a continuation of U.S. patent application Ser. No.13/352,934, filed Jan. 18, 2012, entitled “SYSTEM FOR UNIQUELYIDENTIFYING SUBJECTS FROM A TARGET POPULATION,” now U.S. Pat. No.8,438,997, which is a continuation of U.S. patent application Ser. No.12/716,083, filed Mar. 2, 2010, entitled “SYSTEM FOR UNIQUELYIDENTIFYING SUBJECTS FROM A TARGET POPULATION,” now U.S. Pat. No.8,113,151, which is a continuation of U.S. patent application Ser. No.10/498,188, filed Jun. 8, 2004, entitled “SYSTEM FOR UNIQUELYIDENTIFYING SUBJECTS FROM A TARGET POPULATION,” now U.S. Pat. No.7,841,300, which claims priority from PCT application PCT/US2003/035581,filed Nov. 6, 2003, which claims priority from U.S. ProvisionalApplication Ser. No. 60/424,917, filed Nov. 8, 2002. All of theseapplications are hereby incorporated by reference into this disclosurein their entirety.

FIELD OF THE INVENTION

The invention relates to the field of natural resource management andmore specifically to the automated identification of individual subjectsin a target population to perform population and/or biologicalmonitoring.

BACKGROUND OF THE INVENTION

It is a problem in the field of natural resource management to performpopulation and/or biological monitoring to uniquely identify anindividual subject in a target population. In particular, while work hasbeen done to differentiate among various species in a study area, inorder to produce a gross count of the members of each species in thestudy area, there is a lack of any system that can automatically andaccurately differentiate among the individual members of a particularspecies in a target population.

It is well known that the species in various taxa have externalcharacteristics that are useful in uniquely identifying an individualmember of the species. This process of identifying an individual memberof the species is commonly done on a manual basis, more commonly in themanagement of mammals, where the herd managers can visually distinguishamong members of a particular herd. This process is experientially basedin that the herd manager over time remembers various characteristicsthat distinguish members of the managed herd. However, this knowledge istypically not recorded in any formal manner and is therefore not simplytransferable to others. In addition, the knowledge base of a particularherd is not extensible to any other herd and the data collection andmember recognition process must be laboriously reinitiated when a herdmanager encounters a new herd or even additions to an existing herd.Finally, the identification process is not rigorous, in that it issubject to the personal determinations of the herd manager, may not bebased on scientifically accurate distinctions, and may be limited toonly gross identifying characteristics that are easily recognized.

In the case of very large populations of subjects, where the datacollection and subject distinguishing processes are beyond a simplevisual and human-based memory process, there is presently no method toenable the recognition of an individual member of the population. Inthese populations, the process in identifying the individual must beautomatic, rapid and accurate and extensible to any size population orany population among a larger set of populations. In addition, anyrecognition process must be able to account for temporal and growthchanges in the members of the population, such as seasonal variationsand changes associated with aging. In addition, in order to distinguishamong members of a very large population, a scientifically rigorousprocess must be used and is typically based on subtle and/or complexdifferentiable characteristics.

SUMMARY

One aspect of the invention relates to a system for uniquely identifyingsubjects from a target population which operates to acquire, process andanalyze images of subjects to create data which contains indiciasufficient to uniquely identify an individual in a population ofinterest. The process used by the system in identifying the individualis automatic, rapid and accurate.

There are various species of interest in the field of natural resourcemanagement which have external characteristics that are useful inuniquely identifying an individual member of the species. Thesecharacteristics can be any recognizable feature or pattern of features,which in total have sufficient specificity to create a “signature” thatis unique to the selected individual. These selected characteristics aretypically members of the class of identifying characteristics thatinclude: overall size, color, color/spot/stripe features, overallpatterning, size and shape of physical features, features ofhorns/tusks/antlers, maturity-juvenile/adolescent/adult, scars,deformities, and the like.

The present system for uniquely identifying subjects from a targetpopulation implements an automated, image-based process that capturesdata indicative of a selected set of external characteristics forsubjects that are members of a target population of a predeterminedspecies. The captured data is then processed to create a database recordwhich is associated with the selected subject and which can be used touniquely identify the selected subject. The subject identificationprocess is repeatable, so any subject can be accurately and repeatedlymapped to their associated database record, which database record canthen be updated to reflect changes in the subject and/or presence of thesubject in a target population.

The system for uniquely identifying subjects from a target populationoperates to acquire, process and analyze images to create data whichcontains indicia sufficient to uniquely identify an individual in apopulation of interest. This system implements an automated, image-basedprocess that captures data indicative of a selected set of externalcharacteristics for subjects that are members of a target population ofa predetermined species.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates, in block diagram form, the overall architecture of atypical embodiment of the present system for uniquely identifyingsubjects from a target population and an environment in which it isoperable.

FIG. 2 illustrates, in flow diagram form, the operation of a typicalembodiment of the present system for uniquely identifying subjects froma target population to capture an image of a selected subject.

FIG. 3 illustrates, in flow diagram form, the operation of a typicalembodiment of the present system for uniquely identifying subjects froma target population to process the captured image and uniquely identifythe selected subject.

FIGS. 4 & 5 illustrate typical images that are captured for a selectedsubject for a species, such as rainbow trout.

FIGS. 6 & 7 illustrate typical patterns of characteristics that areidentified for the selected subject illustrated in FIGS. 4 & 5.

FIGS. 8 and 9 illustrate two alternative embodiments of the subjectpositioning apparatus and image capture apparatus for a selectedspecies.

DETAILED DESCRIPTION

FIG. 1 illustrates, in block diagram form, the overall architecture of atypical embodiment of the present system for uniquely identifyingsubjects from a target population (termed “subject identificationapparatus” herein) and an environment in which it is operable. Thesubjects are typically confined in a predetermined location, such as anenclosure 101, which enables the operator of the subject identificationapparatus 100 to manage and select individual subjects foridentification. The enclosure 101 can optionally be connected to asubject isolation apparatus 102 which enables the operator to separateone or more selected subjects from the target population confined in theenclosure 101. When the operator selects an individual subject, thesubject is either automatically or manually oriented within the subjectidentification apparatus 100 for execution of the subject identificationprocess. Once this process is completed, the selected subject isreleased into an exit apparatus 103 which serves to deliver the releasedselected subject to another site or location.

The subject identification apparatus 100 is comprised of a number ofelements. In particular, a subject positioning apparatus 111 can be usedto present the selected subject in a predetermined orientation and/orposition to enable the imaging apparatus 112 to capture image data whichenables the subject identification apparatus 100 to uniquely identifythe selected subject. Alternatively, the present system can be used toprocess images that would be taken by field biologists ofmarginally-restrained (hand-held) or even free-ranging animals such asfree-ranging giraffes or hand-held fish. Alternatively, a “lay” laborforce of anglers can be used to take pictures of the fish they catch andrelease or “bird-watchers” can photographically verify the birds theyview and add to their “life-lists”. The captured image data istransmitted from the imaging apparatus 112 to a processor 113, whichexecutes a database routine 115 that serves to create a database recordfor the selected subject and store the database record in a memory 114,where the database record includes the captured image data and/orprocessed image data that comprises a set of indicia that serves touniquely identify the selected subject. The processor 113 can also storeadditional data in memory 114, such as: date, time, location, subjectmeasurement data (e.g. weight, age, physical measurements, automaticallyreceived GPS data, etc), and the like as part of the database record forthe selected subject. The processing of the captured image data can bein real time as the selected subject is positioned in the imagingapparatus 112 or off-line, after the selected subject is released fromthe imaging apparatus 112.

Furthermore, the imaging apparatus 112 can consist of a number ofelements that are cooperatively operative to generate the captured imagedata. These elements are a function of the species of subject that is tobe imaged as well as the selected set of characteristics that are to bemonitored to create the signature that uniquely identifies the selectedsubject. Within these general considerations, it is expected that thereare numerous implementations possible for the imaging apparatus 112 andthe description of any such apparatus herein represents an illustrationof such apparatus and should not be construed as any limitation of theinventive concepts described and claimed herein. Therefore, at anarchitectural level, it is expected that the imaging apparatus 112 wouldtypically include a subject illumination apparatus 121 to provide thedesired illumination and/or contrast, if it is determined that theambient light is insufficient, to enable the subject identificationapparatus 100 to accurately determine the presence of and performmeasurements of the selected characteristics for this species ofsubjects. In addition, an image capture device 122, such as a digitalcamera or other such image rendering apparatus, is used to determine thepresence of and perform measurements of the selected characteristics forthis species of subjects. Optionally, a display device 123 is providedto enable the operator to view and monitor the operation of the imagecapture device and controls 124 can be provided to further enable theoperator to regulate the operation of the image capture device 122and/or the positioning of the subject within the subject positioningapparatus 111.

System Operation—Image Capture

FIG. 2 illustrates, in flow diagram form, the operation of a typicalembodiment of the present system for uniquely identifying subjects froma target population 100 to capture an image of a selected subject. Thereare numerous alternative implementations of this process and thefollowing description is simply to illustrate a typical application ofthe present system for uniquely identifying subjects from a targetpopulation. Depending upon the nature of the subject and the purpose ofthe image generation process, the subject may simply be imaged or it maybe imaged and identified (such as by tagging), or it may be imaged andcompared with the data stored in a database to determine whether it haspreviously been imaged. The imaging process can be incorporated into apopulation management process, where the subject is uniquely identified,then additional measurements taken on the subject to populate an entryin the population database to track various criteria related to thesubject, such as size, weight, and the like. These “additionalmeasurements” are automatically extracted from the image using imageprocessing and pattern recognition techniques. The photographer mayprovide physical data, but for most of the morphometrics of the animal;the present system for uniquely identifying subjects from a targetpopulation automatically extracts the additional measurements. Also,gender in many cases is automatically recognized.

The typical imaging process is initiated at step 201, where a subject isselected from the population and, at step 202, the subject is positionedor oriented to enable a clear view of the subject. At least one image ofthe subject is captured by image capture device 122 at step 203, ofwhich at least one image is typically in the form of a digital image.The image can optionally be reviewed by an operator on display device123 at step 204 to ensure that there is sufficient quality of the imageand number of features of the desired characteristic contained in theimage to enable the subject identification process to accurately renderan identification of the subject. If the quality and/or content of thecaptured image are inadequate, processing returns to step 202 (or 203)and the image capture process is reactivated. If the image quality andcontent are adequate, a determination is made at step 205 whether thesubject is simply to be added to the database as a newly acquired imageor whether it must be determined whether the subject has previously beenentered into the database. If this is a newly acquired image, thesubject is then further processed at step 206, additional data, if any,associated with the further processing entered into the database in adatabase record associated with this subject and the subject is thenreleased at step 207. The data generated from the image generationprocess is stored in memory 114 at step 208 for processing at step 209either contemporaneously with the image capture process or off line as abackground process. In fact, if the image processing iscontemporaneously done with image capture, the image processing step canoptionally signal the operator when a successful image processing hasbeen effected and the subject can be released.

If it is determined at step 205 that this subject imaging process mayinclude subjects already recorded in the database, then it must bedetermined whether this subject has previously been entered into thedatabase, and processing advances to step 210 where the data generatedfrom the image generation process is stored in memory at step 211 forprocessing at step 212, as described below. The image processing at step213 either returns an indication that the subject is a newly acquiredsubject, at which point processing advances to step 206 as describedabove, or an indication that the subject matches an existing databaserecord for this subject, at which point the existing database recordassociated with this subject is identified and all additional datagenerated in this process is appended to the existing database record.Processing now advances to step 206 as described above.

Example of Selected External Characteristics of a Selected Species

As noted above, it is well known that the species in various taxa haveexternal characteristics that are useful in uniquely identifying anindividual member of the species. These characteristics can be anyrecognizable feature or pattern of features, which in total havesufficient specificity to create a “signature” that is unique to theselected individual. These selected characteristics are typicallymembers of the class of identifying characteristics that include:overall size, color, color/spot/stripe, overall patterning, size andshape of physical features, particularities of horns/tusks/antlers,maturity-juvenile/adole-scent/adult, scars, deformities, and the like.The features within a characteristic are the specific uniqueimplementations of the characteristic.

As an example, the rainbow trout (Oncorhynchus Mykiss) has thecharacteristic color/spot/stripe which has as its set of features thedetails of the colors/spots/stripes, for example, the location and/orsize and/or color of the spots. In the characteristic of overallpatterning, the features are not individual datum points, such aslocation of a spot, but instead are the pattern details. For example,the brook trout (Salvelinus fontinalis) has as one of its visualcharacteristics vermiculations on its back. The pattern ofvermiculations does not have ‘spots’ per se, and unless considered atthe discrete pixel level, it does not have ‘multiple datum points.’ Inthis case, the totality of the differences in intensity is the ‘pattern’that distinguishes subjects. The plurality of features in this instanceis therefore the plurality of differences in intensity.

Thus, an external characteristic, that contains a sufficient number offeatures to provide a unique signature for the subject, can be used toidentify the subject. The definition of “characteristic” and “features”are a function of the species and these terms are used herein to providea consistent structure in describing the operation of the present systemfor uniquely identifying subjects from a target population.

In order to illustrate the operation of the present system for uniquelyidentifying subjects from a target population, a species of fish—rainbowtrout (Oncorhynchus Mykiss)—is used as an example. In this case, onecharacteristic of interest is the spot pattern on the fish, and thefeatures of this characteristic are the plurality of spots that areimaged. Subjects can be located or confined in a monitoring area ormanually positioned on an image platen to thereby capture an image ofthe subject or pertinent section of the subject that contains thedesired characteristics. For example, fish can be captured using fyketraps, electro fishing, seines, trawls, angling and the like.

FIGS. 4 & 5 illustrate typical images that are captured for a selectedsubject for a species, such as rainbow trout and FIGS. 6 & 7 illustratetypical patterns of characteristics that are identified for the selectedsubject illustrated in FIGS. 4 & 5 as part of the imaging process. Inthe case of a fish, the components of the image in order of priority aretypically:

-   -   1.) Fish as separated from background (function of        background/background color/lighting)    -   2.) Components of the head:        -   a. nares (nasal passages)        -   b. head length—anterior and posterior margins        -   c. spot pattern

Given this ordered priority of components, a determination must be maderegarding the characteristics captured by the image that are required toenable the subject identification apparatus 100 to define a signaturethat uniquely identifies the subject. This determination includes theuse of color in the image as an alternative to gray scale intensity.With color, the image data can be filtered to thereby enhance thedistinction between datum points of the features of the selectedcharacteristic(s) and non-essential image data. Other components of thefeatures can include: size, location of the spot on the image,roundness—shape of the spot, other features. All of these features mustbe oriented in a three dimensional space using the imaging features:roll, pitch, yaw. Therefore, in addition to determining imagecharacteristics, the subject must be oriented using either automaticpositioning or manual positioning—which typically entails projecting asuperimposed grid on the subject to facilitate manual positioning of thesubject with relation to the image capture apparatus.

System Implementations for a Selected Species

FIGS. 8 and 9 illustrate two alternative embodiments of the subjectpositioning apparatus and image capture apparatus for the above-notedselected species.

In FIG. 8, an optically transparent tube 801 is used as the subjectpositioning apparatus 111 and creates a constrained path through whichthe selected subject 802 (fish) is transported. The opticallytransparent tube 801 is positioned juxtaposed to at least one digitalcamera that comprises a part of the imaging apparatus 112. Preferably,two digital cameras 811, 812 are used, positioned orthogonal to eachother, to obtain top and side views of the subject 802 once it ispositioned in front of the digital camera(s). In addition, two 9 laserarrays 813, 815 can be used to project positioning grids 814, 816 intothe image frame. One array 815 from the side projects a grid on to theside of the fish's head and provides roll and yaw information. Thesecond array 813 projects a grid from the top and provides pitchinformation. The superimposed grids also provide quantitative dataregarding the curvature of the fish, which enables the captured image tobe projected on a flat reference plane. A lighting source (not shown)can be included to illuminate the subject when it is positioned in frontof the digital cameras. This apparatus is particularly useful incapturing image data for juvenile fish, where manual handling wouldpotentially cause injury to the fish.

In FIG. 9, an adult fish model of the subject positioning apparatus 111is illustrated. This apparatus comprises a platen 901 and three lasergrid projection arrays 911, 912, 913 positioned orthogonal to oneanother. The platen 901 is positioned juxtaposed to at least one digitalcamera 921 that comprises a part of the imaging apparatus 112. Alighting source 922 (shown as a ring flash) can be included toilluminate the subject 902 when the subject is positioned in front ofthe digital camera. The light source may not be necessary and if used,can be any of the numerous types of illumination apparatus, including anelectronic flash, incandescent bulbs, light box, etc. These laser gridprojection arrays 911-913 superimpose a grid 931-933 of preciselypositioned “dots” on the fish's (animal's) head (body). By examining thedistortion of the projected grid, the exact position and shape of thefish's (animal's) head (body) can be determined from the images. Thisallows the system to “unwrap” the curved head/body, as well as correctfor 3-space positioning (roll, pitch and yaw) of the fish/animal. Whilenot necessary for matching/identification, this added informationgreatly facilitates machine-autonomous operations. Furthermore, itgreatly speeds up the feature extraction (quantifying the pattern) andpattern recognition (finding/matching the earlier image in thedatabase).

System Operation—Image Processing

The database is populated with a plurality of data records, each ofwhich comprises the image data and/or the processed image data. Thematching technique is often referred to as “template matching” whichuses autocorrelation as a method for extracting weak signals from anoisy channel. Many image processing techniques are devoted to the sameprinciple—extracting the “signal” from “noisy” image. Theautocorrelation process used in signal processing is exactly(mathematically) the same process applied to images. In the case ofimages, it is applied to each line of the image both horizontally andvertically. For example, in a 1K×1K image, the resulting autocorrelationis a (2K−1)×(2K−1) matrix/image, made up of the single-dimensional(signal processing-type) of each horizontal and vertical row/column ofpixels.

FIG. 3 illustrates, in flow diagram form, the operation of a typicalembodiment of the present system for uniquely identifying subjects froma target population to process the captured image and uniquely identifythe selected subject. At step 301, the operator (or the system in theautomatic mode of operation) positions the subject in the imagingapparatus 112 and at step 302 extracts an Area-Of-interest (AOI), suchas a geometric shaped site, typically a rectangle, from the image of thenew/unknown subject. This Area-Of-Interest is specific to the species orrace of subject, and is determined in the preliminaryevaluation/examination of the species. In the specific case of Rainbowtrout, this Area-Of-Interest is typically delimited by the nares and theposterior terminus of the head. The operator (this term is used hereinbut is intended to be inclusive of an automated system operation)determines at step 303 whether the originally selected Area-Of-Interestis inadequate for the pattern recognition purposes. If not, processingadvances to step 305. If so, at step 304, the operator resets theArea-Of-Interest and selects an “appropriate” Area-Of-Interest with the“best” (most mathematically discernable) pattern and processing returnsto step 302. Since almost any region on the fish/animal can be used todefine/determine the characteristic of a spot pattern that can beautomatically identified, the selection of Area-Of-Interest is more oneof administrative selection for consistency purposes.

At step 305, the operator can resize the Area-Of-Interest to a lowerresolution to facilitate increased speed of processing the capturedimage data. The typical autocorrelation process requires 2n.sup.2floating point operations and therefore a reduction in the number offeatures (spots) comprising the selected characteristic (spot pattern)that must be identified significantly improves the processingefficiency. In the specific case of the Rainbow trout, there is a lowerlimit to the uniqueness of the spot pattern and discriminatingthresholds become difficult to determine with fewer than three spots ifonly a correlation process is used. However, combining correlation withadditional image data processing features can improve the signaturedetermination and processing speed. Therefore, at step 306, anadditional process can be activated, such as determining specificoptical centers of each spot (exact x, y location of the optical centerof the spot in the Area-Of-Interest), which elevates the discriminatoryability of the system as a whole. Augmenting the correlation processwith additional image data processing routines representsspecies-specific “tweaking”. Thus, components of the features of theselected characteristic(s) can be used as well as more than onecharacteristic to obtain the unique signature for the subject.

At step 307, the processor 113 of the subject identification apparatus100 accesses the database of Area-Of-Interest data for this species,using database process 115 and selects appropriate data records from thedatabase of data records for comparison with the presently determinedpattern for the selected subject. At step 308, the processor performsthe correlation operation 116 to determine the correspondence betweeneach selected database record representing a known pattern and thepresently determined characteristic for the selected subject. Whencomparing two subjects' characteristics using this method, theArea-Of-Interest for the known characteristics and the presentlydetermined characteristic do not have to be exact, only “close”, sincethe correlation technique allows a certain amount of “flexibility” inselecting the exact Area-Of-Interest and still provides excellentmatching ability. At step 309, the processor 113 determines whether theresulting correlation coefficient is below the species-specificpre-selected values (threshold), resulting in a ‘match’ with an existingknown pattern for a previously determined subject as indicated at step310, or a failure to match, resulting in the present subject being addedto the database at step 311.

The characteristic(s) being used for identification purposes need onlybe discernable from the background. The above outlined process, using aspot pattern on the fish as the characteristic, requires that a ‘mask’be made that allows “extraction” of the individual features (spots) andthe characteristic (spot pattern) from the background. This process isgenerally referred to as “feature extraction”. Identifying “real” spotsis no small matter, and a number of techniques can be used to enhancethis process. In particular, using the electromagnetic frequency domain(color) to assist the feature extraction process is a possibility insome species where the frequency response (the “transfer function” inthe electromagnetic domain) of the photographic sensor is selected or aspectral filter is used to isolate “real” features and reject thebackground or anomalies that could be mistaken for features.

Instead of pixelization of the pattern from the image data, a Euclidiandistance computation can be done in a polar coordinate system which usesa point of reference and computes the angle from a reference line andthe Euclidian distance from the point of reference to the optical centerof each selected spot. However, such an alternative system is lessamenable to automation and simplified processing of the image data whenthe subject has a large number of spots. However, this method may havemerit in dealing with other species of subjects where thecharacteristics have traits that require Euclidian distancecomputations, such as determination of the antlers on ungulate species.Furthermore, there are numerous pattern matching techniques that can beused and these are well published and within the knowledge of one ofordinary skill in the art. Therefore, an extensive discourse regardingthese techniques is unnecessary for the purposes of describing thepresent subject identification apparatus 100 and is omitted in theinterest of simplicity of description.

What is claimed is:
 1. A system configured to uniquely identifyindividual subjects from a group of subjects, the system comprising:electronic memory configured to store a plurality of records, whereineach record corresponds to an individual subject and comprises a featureof one or more characteristics of the individual subject; and one ormore processors programmed with computer-executable instructions that,when executed by the one or more processors, program the one or moreprocessors to: obtain a digital image of a current subject that capturesa current feature of the one or more characteristics of the currentsubject; for each record of the plurality of records, compare thecurrent feature of the one or more characteristics captured in thedigital image and the feature stored in each record; responsive to adetermination that the current feature of the one or morecharacteristics captured in the digital image does not match any of thefeatures of the one or more characteristics stored in the plurality ofrecords, create a second record corresponding to the current subject;and store the second record in the electronic memory such that theplurality of records includes the second record.
 2. The system of claim1, wherein the one or more characteristics include one or more ofoverall size, color, shape, patch, spot and/or stripe markings, horns,antlers, tusks, or scars.
 3. The system of claim 1, wherein to createthe second record, the one or more processors are further programmed tostore the current feature as at least part of the second record.
 4. Thesystem of claim 3, wherein the one or more processors are furtherprogrammed to: process the digital image to obtain one or moreadditional measurements, separate from the current feature, of thecurrent subject; and add the one or more additional measurements to thesecond record.
 5. The system of claim 4, wherein the additionalmeasurements include a size of the current subject and/or a weight ofthe current subject.
 6. The system of claim 3, wherein the one or moreprocessors are further programmed to: obtain additional data associatedwith the current subject; and add the additional data to the secondrecord.
 7. The system of claim 6, wherein the additional data includes adate associated with the digital image, a time associated with thedigital image, and/or a location associated with the digital image. 8.The system of claim 1, wherein the one or more processors are furtherprogrammed to: cause the current subject to be deposited into an exitapparatus, wherein the current subject is able to exit through the exitapparatus.
 9. The system of claim 1, further comprising an image capturedevice configured to capture the digital image of the current feature ofthe one or more characteristics of the current subject.
 10. The systemof claim 9, wherein the one or more processors are communicativelylinked with the image capture device, and wherein the one or moreprocessors are configured to obtain the digital image by receiving thedigital image from the image capture device.
 11. The system of claim 1,wherein the plurality of individual subjects belong to a plurality ofspecies, and wherein the processor is configured to determine a speciesof the current subject based on a user selection of the species of thecurrent subject.
 12. A method for uniquely identifying individualsubjects from a group of subjects, the method being implemented in acomputer that includes one or more processors programmed withcomputer-executable instructions that, when executed by the one or moreprocessors, program the one or more processors to perform the method,the method comprising: storing, by the one or more processors, in anelectronic memory, a plurality of records, wherein each recordcorresponds to an individual subject and comprises a feature of one ormore characteristics of the individual subject; obtaining, by the one ormore processors, a digital image of a current subject that captures acurrent feature of the one or more characteristics of the currentsubject; for each record of the plurality of records, comparing, by theone or more processors, the current feature of the one or morecharacteristics captured in the digital image and the feature stored ineach record; responsive to a determination that the current feature ofthe one or more characteristics captured in the digital image does notmatch any of the features of the one or more characteristics stored inthe plurality of records, creating, by the one or more processors, asecond record corresponding to the current subject; and storing, by theone or more processors, the second record in the electronic memory suchthat the plurality of records includes the second record.
 13. The methodof claim 12, wherein the one or more characteristics include one or moreof overall size, color, shape, patch, spot and/or stripe markings,horns, antlers, tusks, or scars.
 14. The method of claim 12, wherein tocreate the second record, the one or more processors are furtherprogrammed to store the current feature as at least part of the secondrecord.
 15. The method of claim 14, the method further comprising:processing, by the one or more processors, the digital image to obtainone or more additional measurements, separate from the current feature,of the current subject; and adding, by the one or more processors, theone or more additional measurements to the second record.
 16. The methodof claim 15, wherein the additional measurements include a size of thecurrent subject and/or a weight of the current subject.
 17. The methodof claim 14, the method further comprising: obtaining, by the one ormore processors, additional data associated with the current subject;and adding, by the one or more processors, the additional data to thesecond record.
 18. The method of claim 17, wherein the additional dataincludes a date associated with the digital image, a time associatedwith the digital image, and/or a location associated with the digitalimage.
 19. The method of claim 12, the method further comprising:causing, by the one or more processors, the current subject to bedeposited into an exit apparatus, wherein the current subject is able toexit through the exit apparatus.
 20. The method of claim 12, whereinobtaining the digital image of the current subject comprises: capturing,by an image capture device, the digital image of the current feature ofthe one or more characteristics of the current subject.
 21. The methodof claim 20, wherein the one or more processors are communicativelylinked with the image capture device, the method further comprising:obtaining, by the one or more processors, the digital image by receivingthe digital image from the image capture device.
 22. The method of claim12, wherein the plurality of individual subjects belong to a pluralityof species, the method further comprising: determining, by the one ormore processors, a species of the current subject based on a userselection of the species of the current subject.